Eliminating Redundant and Less-Informative RSS News Articles Based on Word Similarity and a Fuzzy Equivalence Relation
RSS feeds, fuzzy equivalence, filtering news articles
The Internet has marked this era as the information age. There is no precedent in the amazing amount of information, especially network news, that can be accessed by Internet users these days. As a result, the problem of seeking information in online news articles is not the lack of them but being overwhelmed by them. This brings huge challenges in processing online news feeds, e.g., how to determine which news article is important, how to determine the quality of each news article, and how to filter irrelevant and redundant information. In this paper, we propose a method for filtering redundant and less-informative RSS news articles that solves the problem of excessive number of news feeds observed in RSS news aggregators. Our filtering approach measures similarity among RSS news entries by using the Fuzzy-Set Information Retrieval model and a fuzzy equivalent relation for computing word/sentence similarity to detect redundant and less-informative news articles.
Original Publication Citation
Ian Gracia and Yiu-Kai Ng. "Eliminating Redundant and Less-Informative RSS News Articles Based on Word Similarity and A Fuzzy Equivalence Relation." In Proceedings of the 18th IEEE International Conference on Tools with Artificial Intelligence (ICTAI-26), pp. 465-473, November 13-15, 26, Washington, D.C.
BYU ScholarsArchive Citation
Garcia, Ian and Ng, Yiu-Kai D., "Eliminating Redundant and Less-Informative RSS News Articles Based on Word Similarity and a Fuzzy Equivalence Relation" (2006). Faculty Publications. 282.
Physical and Mathematical Sciences
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